Crowd Sourcing Information to Fulfill User Requests

ABSTRACT

A user request is received from a mobile client device, where the user request includes at least a speech input and seeks an informational answer or performance of a task. A failure to provide a satisfactory response to the user request is detected. In response to detection of the failure, information relevant to the user request is crowd-sourced by querying one or more crowd sourcing information sources. One or more answers are received from the crowd sourcing information sources, and the response to the user request is generated based on at least one of the one or more answers received from the one or more crowd sourcing information sources.

RELATED APPLICATIONS

This application claims priority of U.S. Provisional Application Ser.No. 61/646,831, filed May 14, 2012, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to digital assistants, andmore specifically, digital assistants that provide crowd-sourcedresponses to users' speech-based requests.

BACKGROUND

Just like human personal assistants, digital assistants or virtualassistants can perform requested tasks and provide requested advice,information, or services. An assistant's ability to fulfill a user'srequest is dependent on the assistant's correct comprehension of therequest or instructions. Recent advances in natural language processinghave enabled users to interact with digital assistants using naturallanguage, in spoken or textual forms, rather than employing aconventional user interface (e.g., menus or programmed commands). Suchdigital assistants can interpret the user's input to infer the user'sintent; translate the inferred intent into actionable tasks andparameters; execute operations or deploy services to perform the tasks;and produce output that is intelligible to the user. Ideally, the outputproduced by a digital assistant should fulfill the user's intentexpressed during the natural language interaction between the user andthe digital assistant.

The ability of a digital assistant system to produce satisfactoryresponses to user requests depends on the natural language processing,knowledge base, and artificial intelligence implemented by the system.At any time, a digital assistant may be limited by its particularimplementation, however sophisticated that implementation may be, andfail to produce a satisfactory response to a user's request. Awell-designed response procedure in such a situation can improve auser's experience in interacting with the system and prevent the user'sloss of confidence in the system's service.

SUMMARY

The embodiments disclosed herein provide methods, systems, computerreadable storage medium and user interfaces for a digital assistant tocrowd source assistance or information from one or more externalinformation sources (so-called “crowd sourcing information sources” or“CS information sources”), and generate a response to a user requestbased on the crowd sourced information or assistance. These externalinformation sources, for example, include expert information services,general information sources, and forums where answers to questions areprovided in structured, semistructured, and unstructured forms bymembers of the public. In addition, crowd sourced information andanswers can be stored, e.g., in a crowd-sourced knowledge base, in amanner that facilitates searching based on natural language queries orstructured queries derived from subsequent user requests.

Accordingly, some embodiments provide a method for providing a responseto a user request, the method including, at a computer system includingone or more processors and memory storing one or more programs:receiving a user request from a mobile client device, the user requestincluding at least a speech input and seeks an informational answer orperformance of a task; detecting a failure to provide a satisfactoryresponse to the user request; in response to detecting the failure,crowd-sourcing information relevant to the user request by querying oneor more crowd sourcing information sources; receiving one or moreanswers from the crowd sourcing information sources; and generating aresponse to the user request based on at least one of the one or moreanswers received from the one or more crowd sourcing informationsources.

In some embodiments, crowd-sourcing the information relevant to the userrequest further includes: generating one or more queries based on theuser request; and sending the one or more queries to the one or morecrowd sourcing information sources.

In some embodiments, at least one of the queries includes an audiorecording of the speech input.

In some embodiments, the crowd-sourcing further comprises identifying,from a set of crowd sourcing information sources, the one or more crowdsourcing information sources to be queried.

In some embodiments, detecting the failure to provide a satisfactoryresponse to the user request further includes determining that aweb-search based on information contained in the user request isunsatisfactory to the user.

In some embodiments, detecting the failure to provide a satisfactoryresponse to the user request comprises receiving feedback from the userthat a previous response provided to the user for the user request wasunsatisfactory.

In some embodiments, detecting the failure to provide a satisfactoryresponse to the user request comprises analyzing logs of the usage ofthe digital assistant.

In some embodiments, the method further includes: prior to thecrowd-sourcing: requesting permission from the user to send theinformation contained in the user request to the one or more crowdsourcing information sources; and receiving permission from the user tosend the information contained in the user request to the one or morecrowd sourcing information sources.

In some embodiments, the method further includes: sending a list of theone or more crowd sourcing information sources to the mobile clientdevice.

In some embodiments, the one or more crowd sourcing information sourcesrepresented in the list are separately selectable by the user.

In some embodiments, the information contained in the user request issent to a real-time answer-lookup database.

In some embodiments, the information contained in the user request issent to one or more non-real-time expert services.

In some embodiments, the method further includes: receiving at least onereal-time answer from a real-time answer-lookup database; upon receiptof the at least one real-time answer, sending to the mobile clientdevice the at least one real-time answer; receiving at least onenon-real-time answer from a non-real-time expert service after receivingthe at least one real-time answer; and upon receipt of the at least onenon-real-time answer, sending to the mobile client device the at leastone non-real-time answer.

In some embodiments, the at least one real-time answer and the at leastone non-real-time answer are presented to the user at different times.

In some embodiments, the method further includes: not receiving anyanswer from at least one of the one or more crowd sourcing informationsources before generating the remedial response.

In some embodiments, the method further includes: when more than oneanswer is received from the one or more crowd sourcing informationsources, ranking the answers in accordance with predetermined criteria.

In some embodiments, the method further includes: selecting a subset ofanswers from the one or more answers in accordance with the ranking.

In some embodiments, the method further includes: providing the responsein speech form to the user.

In some embodiments, receiving the one or more answers from the crowdsourcing information sources further includes: receiving at least one ofthe one or more answers from individual members of the public innon-real-time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an environment in which a digitalassistant operates in accordance with some embodiments.

FIG. 2 is a block diagram illustrating a digital assistant client systemin accordance with some embodiments.

FIG. 3A is a block diagram illustrating a standalone digital assistantsystem or a digital assistant server system in accordance with someembodiments.

FIG. 3B is a block diagram illustrating functions of the digitalassistant shown in FIG. 3A in accordance with some embodiments.

FIG. 3C is a diagram of a portion of an ontology in accordance with someembodiments.

FIG. 4 is a flow chart for a failure management process invokinginformation crowd sourcing to produce a delayed remedial or correctiveresponse in accordance with some embodiments.

FIG. 5 is an information crowd sourcing module of a digital assistant inaccordance with some embodiments.

FIGS. 6A-6C are flow charts illustrating a process for providing aresponse to a user request based on crowd sourced information inaccordance with some embodiments.

FIG. 7 is a diagram illustrating a failure management and crowd-sourcingknowledge base being used offline to improve various mechanisms of thedigital assistant.

Like reference numerals refer to corresponding parts throughout thedrawings.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a block diagram of an operating environment 100 of a digitalassistant according to some embodiments. The terms “digital assistant,”“virtual assistant,” “intelligent automated assistant,” or “automaticdigital assistant,” refer to any information processing system thatinterprets natural language input in spoken and/or textual form to inferuser intent, and performs actions based on the inferred user intent. Forexample, to act on a inferred user intent, the system can perform one ormore of the following: identifying a task flow with steps and parametersdesigned to accomplish the inferred user intent, inputting specificrequirements from the inferred user intent into the task flow; executingthe task flow by invoking programs, methods, services, APIs, or thelike; and generating output responses to the user in an audible (e.g.speech), textual, and/or visual form.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request is eitherprovision of the requested informational answer, performance of therequested task, or a combination of the two. For example, a user may askthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant may answer, “Youare in Central Park near the west gate.” The user may also request theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant may acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user' friends listed in the user's electronic address book. Thereare numerous other ways of interacting with a digital assistant torequest information or performance of various tasks. In addition toproviding verbal responses and taking programmed actions, the digitalassistant can also provide responses in other visual or audio forms,e.g., as text, alerts, music, videos, animations, etc.

An example of a digital assistant is described in Applicant's U.S.Utility application Ser. No. 12/987,982 for “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, the entire disclosure of which isincorporated herein by reference.

As shown in FIG. 1, in some embodiments, a digital assistant isimplemented according to a client-server model. The digital assistantincludes a client-side portion 102 a, 102 b (hereafter “DA client 102”)executed on a user device 104 a, 104 b, and a server-side portion 106(hereafter “DA server 106”) executed on a server system 108. The DAclient 102 communicates with the DA server 106 through one or morenetworks 110. The DA client 102 provides client-side functionalitiessuch as user-facing input and output processing and communications withthe DA-server 106. The DA server 106 provides server-sidefunctionalities for any number of DA-clients 102 each residing on arespective user device 104.

In some embodiments, the DA server 106 includes a client-facing I/Ointerface 112, one or more processing modules 114, data and models 116,and an I/O interface to external services 118. The client-facing I/Ointerface facilitates the client-facing input and output processing forthe digital assistant server 106. The one or more processing modules 114utilize the data and models 116 to determine the user's intent based onnatural language input and perform task execution based on inferred userintent. In some embodiments, the DA-server 106 communicates withexternal services 120 through the network(s) 110 for task completion orinformation acquisition. The I/O interface to external services 118facilitates such communications.

Examples of the user device 104 include, but are not limited to, ahandheld computer, a personal digital assistant (PDA), a tabletcomputer, a laptop computer, a desktop computer, a cellular telephone, asmart phone, an enhanced general packet radio service (EGPRS) mobilephone, a media player, a navigation device, a game console, atelevision, a remote control, or a combination of any two or more ofthese data processing devices or other data processing devices. Moredetails on the user device 104 are provided in reference to an exemplaryuser device 104 shown in FIG. 2.

Examples of the communication network(s) 110 include local area networks(“LAN”) and wide area networks (“WAN”), e.g., the Internet. Thecommunication network(s) 110 may be implemented using any known networkprotocol, including various wired or wireless protocols, such as e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

The server system 108 can be implemented on one or more standalone dataprocessing apparatus and/or a distributed network of computers. In someembodiments, the server system 108 also employs various virtual devicesand/or services of third party service providers (e.g., third-partycloud service providers) to provide the underlying computing resourcesand/or infrastructure resources of the server system 108.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., the DA-client 102) and a server-side portion(e.g., the DA-server 106), in some embodiments, the functions of adigital assistant can be implemented as a standalone applicationinstalled on a user device. In addition, the divisions offunctionalities between the client and server portions of the digitalassistant can vary in different embodiments. For example, in one exampleembodiment, the DA client can be a thin-client that provides onlyuser-facing input and output processing functions, and delegates allother functionalities of the digital assistant to a backend server.

As described later in this specification, the digital assistant canimplement a crowd sourcing functionality. The crowd sourcingfunctionality allows the digital assistant to gather information fromthird party information sources (i.e., so-called “crowd-sourcinginformation sources” or “CS information sources”), and use the crowdsourced information to facilitate request fulfillment, and in somecases, intent inference, in an extended time frame. In some embodiments,the information crowd sourcing is only invoked when other real-timeresponse mechanisms of the digital assistant have failed to produce asatisfactory response to a user request. In some embodiments, theinformation crowd sourcing is available to produce a response to a userrequest without the presence of a prior failure by another responsemechanism of the digital assistant. In some embodiments, the informationcrowd sourcing is performed offline when failures are detected in a userinteraction log, and the crowd sourced information is subsequently usedto improve the response mechanisms of the digital assistant for futureuser requests and interactions.

FIG. 2 is a block diagram of a user-device 104 in accordance with someembodiments. The user device 104 includes a memory interface 202, one ormore processors 204, and a peripherals interface 206. The variouscomponents in the user device 104 are coupled by one or morecommunication buses or signal lines. The user device 104 includesvarious sensors, subsystems, and peripheral devices that are coupled tothe peripherals interface 206. The sensors, subsystems, and peripheraldevices gather information and/or facilitate various functionalities ofthe user device 104.

For example, a motion sensor 210, a light sensor 212, and a proximitysensor 214 are coupled to the peripherals interface 206 to facilitateorientation, light, and proximity sensing functions. Other sensors 216,such as a positioning system (e.g., GPS receiver), a temperature sensor,a biometric sensor, and the like, can also be connected to theperipherals interface 206, to facilitate related functionalities.

A camera subsystem 220 and an optical sensor 222 are utilized tofacilitate camera functions, such as taking photographs and recordingvideo clips. Communication functions are facilitated through one or morewired and/or wireless communication subsystems 224, which can includevarious communication ports, radio frequency receivers and transmitters,and/or optical (e.g., infrared) receivers and transmitters. An audiosubsystem 226 is coupled to speakers 228 and a microphone 230 tofacilitate voice-enabled functions, such as voice recognition, voicereplication, digital recording, and telephony functions.

An I/O subsystem 240 is also coupled to the peripheral interface 206.The I/O subsystem 240 includes a touch screen controller 242 and/orother input controller(s) 244. The touch-screen controller 242 iscoupled to a touch screen 246. The touch screen 246 and the touch screencontroller 242 can, for example, detect contact and movement or breakthereof using any of a plurality of touch sensitivity technologies, suchas capacitive, resistive, infrared, surface acoustic wave technologies,proximity sensor arrays, and the like. The other input controller(s) 244can be coupled to other input/control devices 248, such as one or morebuttons, rocker switches, thumb-wheel, infrared port, USB port, and/or apointer device such as a stylus.

The memory interface 202 is coupled to memory 250. The memory 250 caninclude high-speed random access memory and/or non-volatile memory, suchas one or more magnetic disk storage devices, one or more opticalstorage devices, and/or flash memory (e.g., NAND, NOR).

The memory 250 stores an operating system 252, a communication module254, a graphical user interface module 256, a sensor processing module258, a phone module 260, and applications 262. The operating system 252includes instructions for handling basic system services and forperforming hardware dependent tasks. The communication module 254facilitates communicating with one or more additional devices, one ormore computers and/or one or more servers. The graphical user interfacemodule 256 facilitates graphic user interface processing. The sensorprocessing module 258 facilitates sensor-related processing andfunctions. The phone module 260 facilitates phone-related processes andfunctions. The application module 262 facilitates variousfunctionalities of user applications, such as electronic-messaging, webbrowsing, media processing, Navigation, imaging and/or other processesand functions.

As described in this specification, the memory 250 also storesclient-side digital assistant instructions (e.g., in a digital assistantclient module 264) and various user data 266 (e.g., user-specificvocabulary data, preference data, and/or other data such as the user'selectronic address book, to-do lists, shopping lists, etc.) to providethe client-side functionalities of the digital assistant.

In various embodiments, the digital assistant client module 264 iscapable of accepting voice input, text input, touch input, and/orgestural input through various user interfaces (e.g., the I/O subsystem244) of the user device 104. The digital assistant client module 264 isalso capable of providing output in audio, visual, and/or tactile forms.For example, output can be provided as voice, sound, alerts, textmessages, menus, graphics, videos, animations, vibrations, and/orcombinations of two or more of the above. During operation, the digitalassistant client module 264 communicates with the digital assistantserver using the communication subsystems 224.

In some embodiments, the digital assistant client module 264 utilizesthe various sensors, subsystems and peripheral devices to gatheradditional information from the surrounding environment of the userdevice 104 to establish a context associated with a user input. In someembodiments, the digital assistant client module 264 optionally providesthe context information or a subset thereof with the user input to thedigital assistant server to help infer the user's intent.

In some embodiments, the context information that can accompany the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some embodiments, the context information also includes thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some embodiments, information relatedto the software state of the user device 106, e.g., running processes,installed programs, past and present network activities, backgroundservices, error logs, resources usage, etc., of the user device 104 canalso be provided to the digital assistant server as context informationassociated with a user input.

In some embodiments, the DA client module 264 selectively providesinformation (e.g., user data 266) stored on the user device 104 inresponse to requests from the digital assistant server. In someembodiments, the digital assistant client module 264 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by the digital assistant server 106. Thedigital assistant client module 264 passes the additional input to thedigital assistant server 106 to help the digital assistant server 106 inintent inference and/or fulfillment of the user's intent expressed inthe user request.

In various embodiments, the memory 250 can include additionalinstructions or fewer instructions. Furthermore, various functions ofthe user device 104 may be implemented in hardware and/or in firmware,including in one or more signal processing and/or application specificintegrated circuits.

FIG. 3A is a block diagram of an example digital assistant system 300 inaccordance with some embodiments. In some embodiments, the digitalassistant system 300 is implemented on a standalone computer system. Insome embodiments, the digital assistant system 300 is distributed acrossmultiple computers. In some embodiments, some of the modules andfunctions of the digital assistant are divided into a server portion anda client portion, where the client portion resides on a user device(e.g., the user device 104) and communicates with the server portion(e.g., the server system 108) through one or more networks, e.g., asshown in FIG. 1. In some embodiments, the digital assistant system 300is an embodiment of the server system 108 (and/or the digital assistantserver 106) shown in FIG. 1. It should be noted that the digitalassistant system 300 is only one example of a digital assistant system,and that the digital assistant system 300 may have more or fewercomponents than shown, may combine two or more components, or may have adifferent configuration or arrangement of the components. The variouscomponents shown in FIG. 3A may be implemented in hardware, software,firmware, including one or more signal processing and/or applicationspecific integrated circuits, or a combination of thereof.

The digital assistant system 300 includes memory 302, one or moreprocessors 304, an input/output (I/O) interface 306, and a networkcommunications interface 308. These components communicate with oneanother over one or more communication buses or signal lines 310.

In some embodiments, the memory 302 includes a non-transitory computerreadable medium, such as high-speed random access memory and/or anon-volatile computer readable storage medium (e.g., one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices).

The I/O interface 306 couples input/output devices 316 of the digitalassistant system 300, such as displays, a keyboards, touch screens, andmicrophones, to the user interface module 322. The I/O interface 306, inconjunction with the user interface module 322, receive user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processthem accordingly. In some embodiments, e.g., when the digital assistantis implemented on a standalone user device, the digital assistant system300 includes any of the components and I/O and communication interfacesdescribed with respect to the user device 104 in FIG. 2. In someembodiments, the digital assistant system 300 represents the serverportion of a digital assistant implementation, and interacts with theuser through a client-side portion residing on a user device (e.g., theuser device 104 shown in FIG. 2).

In some embodiments, the network communications interface 308 includeswired communication port(s) 312 and/or wireless transmission andreception circuitry 314. The wired communication port(s) receive andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 314 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications may use any of a plurality of communicationsstandards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. The network communications interface 308 enables communicationbetween the digital assistant system 300 with networks, such as theInternet, an intranet and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN) and/or ametropolitan area network (MAN), and other devices.

In some embodiments, memory 302, or the non-volatile and/ornon-transitory computer readable storage media of memory 302, storesprograms, modules, instructions, and data structures including all of asubset of: an operating system 318, a communications module 320, a userinterface module 322, one or more applications 324, and a digitalassistant module 326. The one or more processors 304 execute theseprograms, modules, and instructions, and reads/writes from/to the datastructures.

The operating system 318 (e.g., Darwin, RTXC, LINUX, UNIX, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

The communications module 320 facilitates communications between thedigital assistant system 300 with other devices over the networkcommunications interface 308. For example, the communication module 320may communicate with the communication interface 254 of the device 104shown in FIG. 2. The communications module 320 also includes varioussoftware components for handling data received by the wireless circuitry314 and/or wired communications port 312.

The user interface module 322 receives commands and/or inputs from auser via the I/O interface 306 (e.g., from a keyboard, touch screen,and/or microphone), and generates user interface objects on a display.

The applications 324 include programs and/or modules that are configuredto be executed by the one or more processors 304. For example, if thedigital assistant system is implemented on a standalone user device, theapplications 324 may include user applications, such as games, acalendar application, a navigation application, or an email application.If the digital assistant system 300 is implemented on a server farm, theapplications 324 may include resource management applications,diagnostic applications, or scheduling applications, for example.

The memory 302 also stores the digital assistant module (or the serverportion of a digital assistant) 326. In some embodiments, the digitalassistant module 326 includes the following sub-modules, or a subset orsuperset thereof: an input/output processing module 328, aspeech-to-text (STT) processing module 330, a natural languageprocessing module 332, a dialogue flow processing module 334, a taskflow processing module 336, a service processing module 338, a failuremanagement module 340, and a crowd sourcing module 342. Each of theseprocessing modules has access to one or more of the following data andmodels of the digital assistant 326, or a subset or superset thereof:ontology 360, vocabulary index 344, user data 348, task flow models 354,service models 356, and crowd-sourced knowledge base 358.

In some embodiments, using the processing modules, data, and modelsimplemented in the digital assistant module 326, the digital assistantperforms at least some of the following: identifying a user's intentexpressed in a natural language input received from the user; activelyeliciting and obtaining information needed to fully infer the user'sintent (e.g., by disambiguating words, names, intentions, etc.);determining the task flow for fulfilling the inferred intent; andexecuting the task flow to fulfill the inferred intent. In someembodiments, the digital assistant also takes appropriate actions when asatisfactory response was not or could not be provided to the user forvarious reasons.

As shown in FIG. 3B, in some embodiments, the I/O processing module 328interacts with the user through the I/O devices 316 in FIG. 3A or with auser device (e.g., a user device 104 in FIG. 1) through the networkcommunications interface 308 in FIG. 3A to obtain user input (e.g., aspeech input) and to provide responses to the user input. The I/Oprocessing module 328 optionally obtains context information associatedwith the user input from the user device, along with or shortly afterthe receipt of the user input. The context information includesuser-specific data, vocabulary, and/or preferences relevant to the userinput. In some embodiments, the context information also includessoftware and hardware states of the device (e.g., the user device 104 inFIG. 1) at the time the user request is received, and/or informationrelated to the surrounding environment of the user at the time that theuser request was received. In some embodiments, the I/O processingmodule 328 also sends follow-up questions to, and receives answers from,the user regarding the user request. When a user request is received bythe I/O processing module 328 and the user request contains a speechinput, the I/O processing module 328 forwards the speech input to thespeech-to-text (STT) processing module 330 for speech-to-textconversions.

The speech-to-text processing module 330 receives speech input (e.g., auser utterance captured in a voice recording) through the I/O processingmodule 328. In some embodiments, the speech-to-text processing module330 uses various acoustic and language models to recognize the speechinput as a sequence of phonemes, and ultimately, a sequence of words ortokens written in one or more languages. The speech-to-text processingmodule 330 can be implemented using any suitable speech recognitiontechniques, acoustic models, and language models, such as Hidden MarkovModels, Dynamic Time Warping (DTW)-based speech recognition, and otherstatistical and/or analytical techniques. In some embodiments, thespeech-to-text processing can be performed at least partially by a thirdparty service or on the user's device. Once the speech-to-textprocessing module 330 obtains the result of the speech-to-textprocessing, e.g., a sequence of words or tokens, it passes the result tothe natural language processing module 332 for intent inference.

More details on the speech-to-text processing are described inApplicant's U.S. Utility application Ser. No. 13/236,942 for“Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, theentire disclosure of which is incorporated herein by reference.

The natural language processing module 332 (“natural languageprocessor”) of the digital assistant takes the sequence of words ortokens (“token sequence”) generated by the speech-to-text processingmodule 330, and attempts to associate the token sequence with one ormore “actionable intents” recognized by the digital assistant. An“actionable intent” represents a task that can be performed by thedigital assistant, and has an associated task flow implemented in thetask flow models 354. The associated task flow is a series of programmedactions and steps that the digital assistant takes in order to performthe task. The scope of a digital assistant's capabilities is dependenton the number and variety of task flows that have been implemented andstored in the task flow models 354, or in other words, on the number andvariety of “actionable intents” that the digital assistant recognizes.The effectiveness of the digital assistant, however, is also dependenton the assistant's ability to infer the correct “actionable intent(s)”from the user request expressed in natural language.

In some embodiments, in addition to the sequence of words or tokensobtained from the speech-to-text processing module 330, the naturallanguage processor 332 also receives context information associated withthe user request, e.g., from the I/O processing module 328. The naturallanguage processor 332 optionally uses the context information toclarify, supplement, and/or further define the information contained inthe token sequence received from the speech-to-text processing module330. The context information includes, for example, user preferences,hardware and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like.

In some embodiments, the natural language processing is based on anontology 360. The ontology 360 is a hierarchical structure containingmany nodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties”. As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent, a domain concept or entity, or asub-aspect of another property. A linkage between an actionable intentnode and a property node in the ontology 360 defines how a parameterrepresented by the property node pertains to the task represented by theactionable intent node.

In some embodiments, the ontology 360 is made up of actionable intentnodes and property nodes. Within the ontology 360, each actionableintent node is linked to one or more property nodes either directly orthrough one or more intermediate property nodes. Similarly, eachproperty node is linked to one or more actionable intent nodes eitherdirectly or through one or more intermediate property nodes. Forexample, as shown in FIG. 3C, the ontology 360 may include a “restaurantreservation” node (i.e., an actionable intent node). Property node“restaurant,” (a domain entity represented by a property node) andproperty nodes “date/time” (for the reservation) and “party size” areeach directly linked to the actionable intent node (i.e., the“restaurant reservation” node). In addition, property nodes “cuisine,”“price range,” “phone number,” and “location” are sub-nodes of theproperty node “restaurant,” and are each linked to the “restaurantreservation” node (i.e., the actionable intent node) through theintermediate property node “restaurant.” For another example, as shownin FIG. 3C, the ontology 360 may also include a “set reminder” node(i.e., another actionable intent node). Property nodes “date/time” (forthe setting the reminder) and “subject” (for the reminder) are eachlinked to the “set reminder” node. Since the property “date/time” isrelevant to both the task of making a restaurant reservation and thetask of setting a reminder, the property node “date/time” is linked toboth the “restaurant reservation” node and the “set reminder” node inthe ontology 360.

An actionable intent node, along with its linked concept nodes, may bedescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships therebetween) associated with theparticular actionable intent. For example, the ontology 360 shown inFIG. 3C includes an example of a restaurant reservation domain 362 andan example of a reminder domain 364 within the ontology 360. Therestaurant reservation domain includes the actionable intent node“restaurant reservation,” property nodes “restaurant,” “date/time,” and“party size,” and sub-property nodes “cuisine,” “price range,” “phonenumber,” and “location.” The reminder domain 364 includes the actionableintent node “set reminder,” and property nodes “subject” and“date/time.” In some embodiments, the ontology 360 is made up of manydomains. Each domain may share one or more property nodes with one ormore other domains. For example, the “date/time” property node may beassociated with many different domains (e.g., a scheduling domain, atravel reservation domain, a movie ticket domain, etc.), in addition tothe restaurant reservation domain 362 and the reminder domain 364.

While FIG. 3C illustrates two example domains within the ontology 360,other domains (or actionable intents) include, for example, “initiate aphone call,” “find directions,” “schedule a meeting,” “send a message,”and “provide an answer to a question,” and so on. A “send a message”domain is associated with a “send a message” actionable intent node, andmay further include property nodes such as “recipient(s)”, “messagetype”, and “message body.” The property node “recipient” may be furtherdefined, for example, by the sub-property nodes such as “recipient name”and “message address.”

In some embodiments, the ontology 360 includes all the domains (andhence actionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some embodiments, the ontology 360 maybe modified, such as by adding or removing entire domains or nodes, orby modifying relationships between the nodes within the ontology 360.

In some embodiments, nodes associated with multiple related actionableintents may be clustered under a “super domain” in the ontology 360. Forexample, a “travel” super-domain may include a cluster of property nodesand actionable intent nodes related to travels. The actionable intentnodes related to travels may include “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travels” super domain) may have many property nodes incommon. For example, the actionable intent nodes for “airlinereservation,” “hotel reservation,” “car rental,” “get directions,” “findpoints of interest” may share one or more of the property nodes “startlocation,” “destination,” “departure date/time,” “arrival date/time,”and “party size.”

In some embodiments, each node in the ontology 360 is associated with aset of words and/or phrases that are relevant to the property oractionable intent represented by the node. The respective set of wordsand/or phrases associated with each node is the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node can be stored in the vocabulary index 344 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 3B, the vocabulary associated withthe node for the property of “restaurant” may include words such as“food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,”“meal,” and so on. For another example, the vocabulary associated withthe node for the actionable intent of “initiate a phone call” mayinclude words and phrases such as “call,” “phone,” “dial,” “ring,” “callthis number,” “make a call to,” and so on. The vocabulary index 344optionally includes words and phrases in different languages.

The natural language processor 332 receives the token sequence (e.g., atext string) from the speech-to-text processing module 330, anddetermines what nodes are implicated by the words in the token sequence.In some embodiments, if a word or phrase in the token sequence is foundto be associated with one or more nodes in the ontology 360 (via thevocabulary index 344), the word or phrase will “trigger” or “activate”those nodes. Based on the quantity and/or relative importance of theactivated nodes, the natural language processor 332 will select one ofthe actionable intents as the task that the user intended the digitalassistant to perform. In some embodiments, the domain that has the most“triggered” nodes is selected. In some embodiments, the domain havingthe highest confidence value (e.g., based on the relative importance ofits various triggered nodes) is selected. In some embodiments, thedomain is selected based on a combination of the number and theimportance of the triggered nodes. In some embodiments, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

In some embodiments, the digital assistant also stores names of specificentities in the vocabulary index 344, so that when one of these names isdetected in the user request, the natural language processor 332 will beable to recognize that the name refers to a specific instance of aproperty or sub-property in the ontology. In some embodiments, the namesof specific entities are names of businesses, restaurants, people,movies, and the like. In some embodiments, the digital assistant cansearch and identify specific entity names from other data sources, suchas the user's address book, a movies database, a musicians database,and/or a restaurant database. In some embodiments, when the naturallanguage processor 332 identifies that a word in the token sequence is aname of a specific entity (such as a name in the user's address book),that word is given additional significance in selecting the actionableintent within the ontology for the user request.

For example, when the words “Mr. Santo” are recognized from the userrequest, and the last name “Santo” is found in the vocabulary index 344as one of the contacts in the user's contact list, then it is likelythat the user request corresponds to a “send a message” or “initiate aphone call” domain. For another example, when the words “ABC Café” arefound in the user request, and the term “ABC Café” is found in thevocabulary index 344 as the name of a particular restaurant in theuser's city, then it is likely that the user request corresponds to a“restaurant reservation” domain.

User data 348 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. The natural language processor 332 can usethe user-specific information to supplement the information contained inthe user input to further define the user intent. For example, for auser request “invite my friends to my birthday party,” the naturallanguage processor 332 is able to access user data 348 to determine whothe “friends” are and when and where the “birthday party” would be held,rather than requiring the user to provide such information explicitly inhis/her request.

Other details of searching an ontology based on a token string isdescribed in Applicant's U.S. Utility application Ser. No. 12/341,743for “Method and Apparatus for Searching Using An Active Ontology,” filedDec. 22, 2008, the entire disclosure of which is incorporated herein byreference.

Once the natural language processor 332 identifies an actionable intent(or domain) based on the user request, the natural language processor332 generates a structured query to represent the identified actionableintent. In some embodiments, the structured query includes parametersfor one or more nodes within the domain for the actionable intent, andat least some of the parameters are populated with the specificinformation and requirements specified in the user request. For example,the user may say “Make me a dinner reservation at a sushi place at 7.”In this case, the natural language processor 332 may be able tocorrectly identify the actionable intent to be “restaurant reservation”based on the user input. According to the ontology, a structured queryfor a “restaurant reservation” domain may include parameters such as{Cuisine}, {Time}, {Date}, {Party Size}, and the like. Based on theinformation contained in the user's utterance, the natural languageprocessor 332 may generate a partial structured query for the restaurantreservation domain, where the partial structured query includes theparameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in thisexample, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} are notspecified in the structured query based on the information currentlyavailable. In some embodiments, the natural language processor 332populates some parameters of the structured query with received contextinformation. For example, if the user requested a sushi restaurant “nearme,” the natural language processor 332 may populate a {location}parameter in the structured query with GPS coordinates from the userdevice 104.

In some embodiments, the natural language processor 332 passes thestructured query (including any completed parameters) to the task flowprocessing module 336 (“task flow processor”). The task flow processor336 is configured to receive the structured query from the naturallanguage processor 332, complete the structured query, if necessary, andperform the actions required to “complete” the user's ultimate request.In some embodiments, the various procedures necessary to complete thesetasks are provided in task flow models 354. In some embodiments, thetask flow models include procedures for obtaining additional informationfrom the user, and task flows for performing actions associated with theactionable intent.

As described above, in order to complete a structured query, the taskflow processor 336 may need to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,the task flow processor 336 invokes the dialogue processing module 334(“dialogue processor 334”) to engage in a dialogue with the user. Insome embodiments, the dialogue processor 334 determines how (and/orwhen) to ask the user for the additional information, and receives andprocesses the user responses. (The questions are provided to and answersare received from the users through the I/O processing module 328.) Insome embodiments, the dialogue processor 334 presents dialogue output tothe user via audio and/or visual output, and receives input from theuser via spoken or physical (e.g., clicking) responses. Continuing withthe example above, when the task flow processor 336 invokes the dialogueflow processor 334 to determine the “party size” and “date” informationfor the structured query associated with the domain “restaurantreservation,” the dialogue flow processor 335 generates questions suchas “For how many people?” and “On which day?” to pass to the user. Onceanswers are received from the user, the dialogue flow processor 334 canthen populate the structured query with the missing information, or passthe information to the task flow processor 336 to complete the missinginformation from the structured query.

In some cases, the task flow processor 336 may receive a structuredquery that has one or more ambiguous properties. For example, astructured query for the “send a message” domain may indicate that theintended recipient is “Bob,” and the user may have multiple contactsnamed “Bob.” The task flow processor 336 will request that the dialogueprocessor 334 disambiguate this property of the structured query. Inturn, the dialogue processor 334 may ask the user “Which Bob?”, anddisplay (or read) a list of contacts named “Bob” from which the user maychoose.

Once the task flow processor 336 has completed the structured query foran actionable intent, the task flow processor 336 proceeds to performthe ultimate task associated with the actionable intent. Accordingly,the task flow processor 336 executes the steps and instructions in thetask flow model according to the specific parameters contained in thestructured query. For example, the task flow model for the actionableintent of “restaurant reservation” may include steps and instructionsfor contacting a restaurant and actually requesting a reservation for aparticular party size at a particular time. For example, using astructured query such as: {restaurant reservation, restaurant=ABC Café,date=Mar. 12, 2012, time=7 pm, party size=5}, the task flow processor336 may perform the steps of: (1) logging onto a server of the ABC Caféor a restaurant reservation system such as OPENTABLE®, (2) entering thedate, time, and party size information in a form on the website, (3)submitting the form, and (4) making a calendar entry for the reservationin the user's calendar.

In some embodiments, the task flow processor 336 employs the assistanceof a service processing module 338 (“service processor”) to complete atask requested in the user input or to provide an informational answerrequested in the user input. For example, the service processor 338 canact on behalf of the task flow processor 336 to make a phone call, set acalendar entry, invoke a map search, invoke or interact with other userapplications installed on the user device, and invoke or interact withthird party services (e.g. a restaurant reservation portal, a socialnetworking website, a banking portal, etc.). In some embodiments, theprotocols and application programming interfaces (API) required by eachservice can be specified by a respective service model among theservices models 356. The service processor 338 accesses the appropriateservice model for a service and generates requests for the service inaccordance with the protocols and APIs required by the service accordingto the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant can submit a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by the task flow processor 336, the service processor 338can establish a network connection with the online reservation serviceusing the web address stored in the service model, and send thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some embodiments, the natural language processor 332, dialogueprocessor 334, and task flow processor 336 are used collectively anditeratively to infer and define the user's intent, obtain information tofurther clarify and refine the user intent, and finally generate aresponse (i.e., an output to the user, or the completion of a task) tofulfill the user's intent.

In some embodiments, after all of the tasks needed to fulfill the user'srequest have been performed, the digital assistant 326 formulates aconfirmation response, and sends the response back to the user throughthe I/O processing module 328. If the user request seeks aninformational answer, the confirmation response presents the requestedinformation to the user. In some embodiments, the digital assistant alsorequests the user to indicate whether the user is satisfied with theresponse produced by the digital assistant 326.

More details on the digital assistant can be found in the U.S. Utilityapplication Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant”, filed Jan. 18, 2010, U.S. Utility Application No.61/493,201, entitled “Generating and Processing Data Items ThatRepresent Tasks to Perform”, filed Jun. 3, 2011, the entire disclosuresof which are incorporated herein by reference.

In many instances, a digital assistant is able to infer a user's intentbased on a natural language request provided by the user and fulfill theuser's request either by providing information sought by the user'srequest or by performing tasks according to the user's request. However,sometimes, the digital assistant will fail to provide a satisfactoryresponse to the user's request for information or action. The reasonsfor the failures can be many, such as imperfect speech recognition,unrecognized terms and concepts in the user request, incorrect orincomplete information and inadequate capability in the digitalassistant's services, and so on. Regardless of the reason for thedigital assistant's failure to provide a satisfactory response to a userrequest, it is desirable to implement a suitable failure managementprocedure for the digital assistant.

As shown in FIG. 3B, in some embodiments, the digital assistant 326 alsoimplements a failure management module 340 to provide appropriateremedies when a failure is detected. In some embodiments, the failuremanagement module 340 invokes the crowd sourcing module 342 or searchesfor answers in the crowd-source knowledge base 358 to generate anappropriate remedial or corrective response for a failed user request.The crowd sourcing module 342 issues queries and collects answers fromone or more external CS information sources over an extended period oftime, and uses the answers to supplement the digital assistant'sabilities in speech processing, natural language processing (for intentinference), and/or task flow processing. For example, the crowd sourcedanswers may help to recognize speech in particular accents in differentregions, expand the vocabulary associated with different domains, and/oridentify additional domain entities. In some embodiments, the crowdsourced answers are used (either by the digital assistant itself, or bya provider of the digital assistant) to create additional domains andtask flows to further expand the capabilities of the digital assistant.

In some embodiments, the crowd sourcing module 342 establishes andmaintains the crowd sourced knowledge base 358. The crowd sourcedknowledge base 358 stores crowd sourced information that addressesinformational or task requests that the digital assistant might provideto its users. In some embodiments, the contents of the crowd sourcedknowledge base are organized by records of previous user requests towhich the digital assistant had initially failed to successfullyrespond, but subsequently fulfilled using crowd-sourced information. Thecrowd-sourced knowledge base provides references and information to thedigital assistant to provide correct and satisfactory responses to thesame or similar user requests received in the future. In someembodiments, the crowd sourced knowledge base is organized to facilitatesearching by the natural language processor. For example, theinformation and answers in the crowd sourced knowledge base may beindexed by nodes in an ontology (e.g., the ontology 360 in FIG. 3B) aswell, so that the infrastructure of the natural language processor canbe leveraged to quickly find past questions and answers in one or morerelevant domains.

In some embodiments, the failure management module 340, the crowdsourcing module 340 and the CS knowledge base 358 are part of thedigital assistant system 326, and can various other components throughvarious internal application interfaces. In some embodiments, one ormore of the failure management module 340, the crowd sourcing module340, and the CS knowledge base 358, of one or more sub-componentsthereof are optionally provided separately from the digital assistantsystem 326, and the digital assistant system 326 accesses each of thesemodules and sub-components thereof through one or more externalapplication programming interfaces (APIs). In some embodiments, variousaspects of the digital assistant system 326, such as the speech to textprocessing (e.g., the speech modules, acoustic models, vocabulary usedin speech recognition), the natural language processing aspect (e.g.,language models, grammar, ontology, etc.), the task-flow, dialogue flow,and service processing, are modified based on the information stored inthe CS knowledge base 358 to improve future performance of the digitalassistant system 326.

In some embodiments, the digital assistant maintains usage logs 370 onuser requests and interactions between the digital assistant and theusers. The usage logs optionally store information such as the userrequests received, the context information surrounding the userrequests, the responses provided to the users, and feedback provided bythe users, the parameters, models, third-party services, and proceduresused by the digital assistant to generate and provide the responses,etc. In some embodiments, the usage logs are searchable by varioussearch parameters, such as time, location, user, demographic, responsetype, feedback type, task type, duration, failure type, etc. Moredetails are provided with respect to the usage log 370 in FIG. 7 andaccompanying descriptions.

Although FIG. 3B does not explicit show the communication interfacesbetween all components of the digital assistant 326, it is to beunderstood that the components shown are capable of communicate with anyother components of the digital assistant 326 either directly or throughone or more other interfaces, such as application programminginterfaces, database query interfaces, and/or other interfaces,protocols, and/or communication channels.

FIG. 4 is a flow diagram illustrating an example process 400 undertakenby a failure management module of a digital assistant (e.g., the failuremanagement module 340 in FIGS. 3A-3B). The example process 400 is merelyan illustration of the decision process regarding which remedy option(s)should be provided to the user after a failure to provide a satisfactoryresponse is recognized by the digital assistant. Other methods andprocesses are possible, and more or fewer remedy options may beimplemented by the failure management module of the digital assistant invarious embodiments.

In the example process 400, first, the digital assistant registers afailure to provide a satisfactory response to a user request, andoptionally determines the failure type for the failure (402). A failuretype is determined based on the reason for which the digital assistanthas failed to produce a satisfactory response to the user request. Thefailure may be discovered by the digital assistant during the naturallanguage processing or task flow execution process, or may be indicatedby the user after the unsatisfactory response was provided to the user.

After the failure has been registered and its failure type determined,the digital assistant selects one or more real-time remedy optionssuitable for addressing the type of failure, and presents the options tothe user (404). Examples of real-time remedy options include doing a websearch based on the user request, calling a technical support hotline,calling an emergency number, searching the crowd-sourced knowledge base,and the like. In this specification, a real-time response refers to aresponse to a user request provided to the user by the digital assistantwithin a time-frame associated with the same user session or continuousdialogue between the user and the digital assistant in which the userrequest was received by the digital assistant. Therefore, each of thereal-time remedy options should produce a real-time remedial orcorrective response to the user request, e.g., within a few minutes ofthe user request.

In some embodiments, if the digital assistant had been able to partiallyinfer one or more candidate actionable intents during the naturallanguage processing stage, the candidate intents and their associatedproperties and domains can be used by the digital assistant in selectingthe real-time remedy options to be presented to the user. In someembodiments, the domains (or actionable intents) recognized by thedigital assistant may each be associated with one or more real-timeremedy options. In some embodiments, the domains (or actionable intents)are clustered into different groups, and each group is associated withone or more real-time remedy options.

After the selected real-time remedy option(s) are presented to the user,the user may reject or accept the real-time remedy options(s) byproviding a user input (e.g., a verbal reply, a gestural input, or thelike). Based on the user input, the digital assistant determines whetherthe user has accepted any of the real-time remedy option(s) (406). Ifthe digital assistant determines that the user has accepted one or moreof the real-time remedy option(s) presented to the user, the digitalassistant proceeds to execute the accepted real-time remedy option(s)(e.g., performing the web search, or making the call to the technicalsupport hotline, and so on) (408).

Once the accepted real-time remedy options have been executed (e.g.,when search results from the web search have been presented, or when thetechnical support call is completed), the digital assistant inquireswhether the user is satisfied with the results of the real-time remediesjust provided (410). If the digital assistant determines that the useris satisfied with the real-time remedial response based on the user'sresponse, the digital assistant considers that the failure has beenaddressed and ceases further action regarding the failure and associateduser request (412).

In some embodiments, if the digital assistant determines that the userhas rejected all of the real-time remedy options presented to the user(e.g., shown as the “No” branch of the decision 406), or if the user isunsatisfied with the real-time remedial response(s) provided to the user(e.g., shown as the “No” branch of the decision 410), the digitalassistant proceeds to invoke the delayed remedy procedures (414). Thedelayed remedy procedures include consulting with external CSinformation sources and/or crowd sourced knowledge base to generate adelayed remedial or corrective response in an extended time frame.

In this specification, a delayed response refers to a response to a userrequest that is provided outside of the time frame of the current usersession or continuing dialogue with the user in which the user requestwas first received. The time-frame in which a delayed response to a userrequest is provided ranges from several minutes to several hours, days,or weeks, depending on the nature of the user request and the typicaltime frame that useful information may be crowd sourced from external CSinformation sources.

As shown in FIG. 4, the delayed remedy procedures include firstobtaining user's authorization to proceed with information crowdsourcing for the user request (416), performing the information crowdsourcing process (418), and generating a delayed response based on thecrowd-sourced information, as well as any information that the digitalassistant already possessed before the crowd sourcing was started (420).More details on information crowd sourcing for user requests areprovided with reference to FIGS. 5, 6A-6C, and 7.

As set forth above, in some embodiments, the digital assistant mayattempt to obtain additional information from external sources andformulate a response based on the additional information in an extendedtime-frame. In some embodiments, the information crowd sourcingprocedures may also be invoked as one of the means to generate aresponse without first detecting a failure. For example, the digitalassistant can allow the user to explicitly request a response to begenerated based on crowd sourced information at the outset. In responseto the user's explicit request, the natural language processor of thedigital assistant can invoke the information crowd sourcing moduledirectly without first detecting a failure.

FIG. 5 is a block diagram of an example information crowd sourcingmodule (e.g., the crowd sourcing module 342 shown in FIGS. 3A-3B andlater in FIG. 7) in accordance with some embodiments. As shown in FIG.5, the crowd sourcing module 342 includes an information sourceselection module 502, a query generation module 504, an answermonitoring module 506, an answer integration module 508, and a responsegeneration module 510. In some embodiments, the crowd sourcing module342 also includes knowledge building module 512 to build and maintainthe crowd-sourced knowledge base based on the crowd sourced information.

Also shown in FIG. 5, the crowd sourcing module 342 stores data invarious data structures and databases to keep track of the CSinformation sources, user requests, queries, and answers involved in thecrowd sourcing process. For example, the information sources database516 stores the CS information sources available to provide crowd sourcedinformation. The user requests database 518 stores the user requests forwhich information crowd sourcing is currently being performed. Thequeries database 520 stores the queries that have been sent to theexternal CS information sources for each user request. The answersdatabase 522 stores the answers that have been received from the CSinformation sources for each query.

In some embodiments, the information source selection module 502selects, from among multiple CS information sources in the informationsource database 516, one or more CS information sources suitable toprovide useful information for the comprehension and fulfillment of auser request. In some embodiments, the information source selectionmodule is optional, and a fixed set of CS information sources are usedfor all user requests.

In some embodiments, the query generation module 504 generates one ormore queries for each user request for which information crowd sourcingis to be performed. The queries are generated based on the user requestand its context information. The query generation module designs thequeries such that they are likely to bring back answers helpful in thecomprehension and fulfillment of the user request. In some embodiments,the query generation module 504 also serves to send the queries to theappropriate CS information sources.

In some embodiments, the answer monitoring module 506 monitors the CSinformation sources to retrieve answers to queries from the CSinformation sources. In some embodiments, the answer monitoring module506 can also receive answers sent to the answer monitoring module 506 bythe CS information sources. For different CS information sources and/orqueries, the time frame in which monitoring for answers is performed canrange in minutes, hours, days, weeks, or even longer. The answermonitoring module 506 stores the answers received for each query in theanswers database 522, and keeps track of the answer statuses of thequeries.

Once the answer monitoring module determines that sufficient answershave been collected for the queries issued for a particular userrequest, the answer integration module filters, ranks, reconciles, andintegrates the answers to provide consolidated crowd sourced informationrelevant to the particular user request to the response generationmodule. The response generation uses the consolidated crowd sourcedinformation and any information the digital assistant already possessesto generate a response to the particular user request.

In some embodiments, if the response generated based on the crowdsourced information is satisfactory to the user, the knowledge-basebuilding module 512 stores the consolidated crowd sourced informationand/or the queries and answers that contributed to the consolidatedcrowd sourced information in the crowd-sourced knowledge base 358.

FIG. 5 is merely an illustration of how a crowd sourcing module may beimplemented. In various embodiments, more or fewer components may beused to implement information crowd sourcing for the digital assistant.More or fewer functions may be provided by the digital assistant. Moredetails regarding the crowd sourcing module 342 and the informationcrowd sourcing process are provided with reference to FIGS. 6A-6C, and 7below.

FIGS. 6A-6C illustrate an example process 600 for crowd sourcinginformation to provide a response to a user request. In someembodiments, the process 600 can be performed as part of a delayedremedy procedure used when one or more real-time response mechanismshave failed to produce a satisfactory response to the user. In someembodiments, the process 600 is a standalone process that is providedindependently of a detected failure to fulfill a user request. In someembodiments, the process 600 may be used to provide a response eitherwhen a prior failure was detected or without the presence of a priorfailure. FIGS. 6A-6C each describes one stage of the information crowdsourcing process. Not all steps shown in FIGS. 6A-6C are necessary inall embodiments. In some embodiments, the process 600 is performed bythe information crowd sourcing module 358 shown in FIGS. 3A-3B and 5.

FIG. 6A illustrates the first stage of the information crowd sourcingprocess. In the first stage, queries are generated based at least inpart on a user request, and CS information sources are selected forinformation crowd sourcing for the user request. This stage of theinformation crowd sourcing process can happen quickly and within thesame user session in which the user request was first received.

As shown in FIG. 6A, during the first stage, the digital assistant firstseeks express permission from the user that information crowd sourcingis to be used to aid in the generation of a satisfactory response to theuser request (602). In some embodiments, the digital assistant notifiesthe user that the information crowd sourcing does not guarantee toproduce a satisfactory response, and that even if the information crowdsourcing does bring back useful information for producing a satisfactoryresult, it would take some extended time outside of the current usersession before the response can be generated. In some embodiments, thedigital assistant also notifies the user that the digital assistant mayact on behalf of the user to answer questions and provide additionalinformation to external CS information sources during the crowd sourcingprocess. The digital assistant allows the user to reject the option toperform crowd sourcing for the user request. The digital assistant canalso help the user to establish some privacy rules for the digitalassistant for interacting with different CS information sources, suchthat the user's privacy is not inadvertently compromised during thecrowd sourcing.

Based on user's input, the digital assistant determines whether the userhas accepted the option to crowd source information for the user request(604). If the user does not accept the information crowd sourcing optionfor any reason (e.g., for privacy or timing concerns), the digitalassistant notifies the user that a satisfactory response cannot begenerated based on the current capabilities of the digital assistant.After the notification, the digital assistant can cease further actionsor dialogues regarding the user request (606).

If the digital assistant determines that the user would like to proceedwith the information crowd sourcing, the digital assistant proceeds toidentify one or more CS information sources suitable for providinginformation regarding the user request (608). In some embodiments, theCS information source selection is performed by the information sourceselection module 502 shown in FIG. 5. In some implementations, theinformation source selection is optional, and a default set ofinformation sources are used for all user requests.

In some embodiments, the digital assistant optionally selects thesuitable CS information sources based on the properties and domains thatwere “activated” by the words in the user request during the earliernatural language processing of the user request. For example, if theproperties of “restaurant” and “birthday party” were activated duringthe natural language processing of a user request, the digital assistantcan select CS information sources such as a life style informationportal, rather than a technical support information portal. In someembodiments, other criteria for selecting the CS information sources canbe used.

In some embodiments, the CS information sources that the crowd sourcingmodule may query for information and answers include public forums. In apublic forum, questions can be posted to a wide audience, and answerscan be solicited and received from the general public who visit thepublic forum. Examples of public forums include online chat rooms,online message boards, discussion groups, and the like. In general, alarge public forum can have sub-forums focused on different topics andsubject matters. In some embodiments, the digital assistant can treateach sub-forum as a separate CS information source.

In general, public forums are suitable for collecting answers forqueries that are difficult to categorize or comprehend for machines butmay be easily handled by real people. For example, the digital assistantmay fail to comprehend a question or answer such as “How to get rid ofants in my kitchen?” based on the domains and properties it hasimplemented so far, however, individual members of the public willeasily understand the question and may have straight-forward answersright away.

In some embodiments, the CS information sources may include specializedsources providing more specialized and focused information, such asexpert forums, technical support forums, fan-sites for particularsubject matter, and the like. The more specialized and focused CSinformation sources may be more suitable for queries that requirespecialized knowledge. For example, a user may ask, “Why can't I printthis?” after failing to print a webpage opened on her handheld device.The answer may require a diagnostic procedure that onlyspecially-trained technical support staff or other technically savvyindividuals can provide. The query regarding the error in printing awebpage from a user device may be more suited for a technical supportexpert forum than a general purpose public forum or a product reviewexpert forum, for example.

In some embodiments, the CS information sources include a group ofself-identified contributors. Each contributor can be an individual or agroup of individuals who have identified themselves as experts inparticular fields and agreed to answer questions from the crowd sourcingmodule in the particular fields.

For example, a female programmer, who is also an excellent chef, mayidentify herself as an expert in the fields of computers and cookingWhenever a query in one of these two fields is issued by the informationcrowd sourcing module, the programmer can be alerted of the query (e.g.,through an automatic notification system of the digital assistant). Ifthe programmer is able to provide an answer to the query, she can submitthe answer to the information crowd sourcing module (e.g., to the answermonitoring module 506 shown in FIG. 5).

In some embodiments, queries and answers in a particular field may beposted in a public area for all self-identified experts of the field tosee. The answers provided for the queries can be peer reviewed and ratedby other self-identified experts in the field. The information crowdsourcing module can utilize the rating of the answers to select the bestanswers to a query, for example.

In some embodiments, the interaction between the information crowdsourcing module and the self-identified experts can be through athird-party service, where the third-party service handles thedispatching of queries and collection of answers, as well as screeningthe self-identified experts, evaluating answers, and/or rating theself-identified experts. In some embodiments, the information crowdsourcing module implements the interfaces and processing components formanaging the information crowd sourced from the self-identified experts.

In some embodiments, the CS information sources include an answer arenawhere users participate in a game in which participants compete to seewho can provide the best answers and the most number of answers toqueries posted to the game arena. In some embodiments, the game arenaincludes many smaller arenas each for a different question domain orsubject matter. The game arena can provide rewards for gamers, such aspoints, credits, and the like. Sometimes, the game arena can be used tocollect answers for the more challenging questions, and the participantscan utilize various resources they personally have access to as anindividual (e.g., either online or in the real world) to figure out ananswer. The answers for a particular query can be reviewed and/or votedon by a group panel, or by other disinterested/non-participating usersfor the particular query.

In some embodiments, each user can specify a list of preferred CSinformation sources or the digital assistant of the user may havelearned over time that a particular group of CS information sources haveworked well for the user. In such embodiments, the information sourceselection module can choose the suitable CS information sources based onthe user's preference or the recommendation by the digital assistantbased on past successes.

In some embodiments, some CS information sources are only available to aspecific group of users (e.g., subscribers to a premium CS informationsource), and the information source selection can be based on the statusand identity of the user and whether they have authorization to useparticular CS information sources. Other ways of selecting the CSinformation sources for different users and user requests are possible.

Once the digital assistant has selected the suitable CS informationsources for the user request, the digital assistant can proceed togenerate one or more queries based on the user request and any availablecontext information (610). In some embodiments, the query generation isperformed by the query generation module 504 shown in FIG. 5. In someembodiments, the queries may be the raw voice input of the usercontained in the user request. In some embodiments, the query may be aportion of the voice input of the user, and/or other processed form ofthe raw voice input. In some embodiments, the query includes some or allof the context information currently associated with the user request.In some embodiments, the query may also include partially instantiateddomains and/or concepts related to the user request. In variousembodiments, the crowd sourcing module generates different queries fordifferent CS information sources, and according to the respectiveformats required by the different CS information sources.

In some embodiments, the CS information sources identified in theinformation source database 516 can be organized according subjectmatter and types. Different APIs and/or protocols needed to communicatewith each CS information source can be stored in the information sourcedatabase 516 as well. The query generation module 504 can refer to theCS information source database 516 when formatting the queries for aparticular CS information source.

In some embodiments, the query generation module can generate a querythat includes the user request and associated context information theiroriginal data form for some CS information sources. The query generationmodule can also generate a query that includes the user request andcontext information in a processed form according to the requirements ofa CS information source, e.g., according to particular APIs orformatting requirements of the CS information source. In someembodiments, query generation module generates a natural language querythat paraphrases the user request with some useful context information,but has all personally identifiable information removed therefrom.

In some embodiments, the query generation module generates queries thatare related to only one or more sub-aspects of the user request. Forexample, suppose that the user said, “Please make an e-card for me withthe words ‘You are the best dad in the world.’ The query generationmodule may generate a query “How do I make a custom e-card?” The querygeneration module may also generate other queries such as “What is ane-card?” or “How to make an e-card?” if the word “e-card” and task-flowassociated with making an e-card are yet not part of the vocabulary andtask-flow models of the digital assistant at the time. In someembodiments, the query generation module uses the natural languageprocessing capabilities of the digital assistant to generate the queriesin natural language.

In some embodiments, the query generation module can generate queriesthat are natural language variants of the user request. For example,suppose the user said, “Teach me how to cook lobsters.” The querygeneration module may generate natural language queries such as “How tocook lobsters?” “Lobster cooking tips” “Got an easy lobster recipe?” Thecrowd sourcing module can use the natural language variants to identifysimilar questions that have already been asked and answered in the pastin various occasions. For example, instead of or in addition to issuinga fresh query generated from the user request to the various CSinformation sources, the information crowd sourcing module can search onFAQ bulletins, message boards, public forums, and the crowd-sourceknowledge base for similar or equivalent questions, and use the answersto those similar or equivalent questions to help with formulating aresponse to the user request.

In some embodiments, the query generation module identifies userrequests that are very similar to one another and can benefit from theanswers to the same queries. For example, suppose that the crowdsourcing module has dispatched a first query “Printing error aftersystem upgrade to OS version 7.1.” to a CS information source for a userrequest “Why can't I print?” accompanied by context informationindicating a system upgrade to OS version 7.1, and the first query isnow in the answer gathering stage. Further suppose that the informationcrowd sourcing module now needs to do information crowd sourcing for asecond user request, “I upgraded to OS 7.1 and now I can no longerprint, what's wrong?” The query generation module will generate a secondquery based on the second user request, and then recognize that there isalready a similar or equivalent query (i.e., the first query) dispatchedto one or more CS information sources. Therefore, the crowd sourcingmodule does not dispatch the newly generated query to the CS informationsources that have already received a similar or equivalent query before.Instead, the crowd sourcing module waits for the answers to the firstquery to be collected, and uses the answers to the first query forgenerating responses to both user requests.

The ability to recognize that similar or equivalent queries have beendispatched or answered is important because the information crowdsourcing module handles information crowd sourcing for many userrequests received from many users. Many of these user requests may bevery similar and the same queries and answers may provide the necessaryinformation to resolve all of these user requests. Thus, by recognizingsimilarities between user requests, and detecting and filtering outduplicate or nearly duplicate queries generated from the user requests,the information crowd sourcing module can operate more efficiently. Thefiltering of duplicate and near duplicate queries also helps prevent theneed to have contributors answer the same questions over and over again.In some embodiments, the query generation module uses the naturallanguage processing capabilities of the digital assistant to determineif two or more queries are duplicates or near duplicates of eachanother.

In some embodiments, the information crowd sourcing module avoidsissuing duplicate queries by recognizing the commonalities amongdifferent user requests. In some embodiments, the commonality betweentwo user requests can be found based on a large overlap between thedomains and properties activated by the two user requests. If two userrequests can be fulfilled using the same root solutions or answers(e.g., a common task flow), then the information crowd sourcing onlyneed to be performed for one of the two user requests or a genericcombination of the two user requests.

For example, suppose that, after a new version of a device operatingsystem is released, many users who have upgraded to the new version ofoperating system may experience similar technical issues due tocompatibility with existing applications or due to bugs in the operatingsystem. Therefore, multiple users may issue user requests indicating aproblem with their respective devices around the same period of time.The information crowd sourcing module will recognize that the differentuser requests “Why did my web browser crash?” “What happened to my webbrowser?” “Why can't I open this webpage?” and “Why can't I open thislink from my e-mail?” all relate to the same issue because all of thesedifferent user requests map to the same domain of technical questionsrelated to the domain of “web browser application technical issues” inthe ontology implemented by the digital assistant. In addition, sincedevices of the users may collect context information (e.g., theoperation that the user was performing right before the user request wasreceived, the current version of the operating system, the device type,and so on), the commonality of the user requests is further reflected inthe context information provided along with the user requests. In thisexample, the commonality of the user requests will be reflected in thecontext such as the version of the operating system, the time that theuser requests were received, the error log that has been recorded, andso on. When these user requests are all directed to the crowd sourcingmodule around the same time period, the crowd sourcing module will beable to determine and recognize that the information crowd sourcing ofthese similar user requests can be addressed together using one or moregeneric queries, such “How to fix the browser problem after operatingsystem upgrade to version x.x, in device XX?” The version informationand device information in the generic query is completed in by thecontext information accompanying the user requests. In some embodiments,some user-specific context information (anonymized to protect userprivacy) may be utilized by the experts in the CS information sources toprovide answers that may be generally applicable to other users.

In some embodiments, the information crowd sourcing componentestablishes a special query pool for handling queries that are eachrelevant to the fulfillment of multiple similar or equivalent userrequests. The crowd sourcing module optionally sends the queries in thespecial query pool to a team of experts who can provide solutions,answers, or information in a more speedily manner. Once the answer to aquery in the special query pool is received, the answer can help resolvea large number of user requests. In some embodiments, the crowd sourcingmodule establishes certain criteria for determining when a query mayenter the special query pool to receive expedited answers.

The ability to recognize the similarity between user requests alsoallows the information crowd sourcing module to determine if a currentuser request is similar or identical to another user request that hasalready been successfully fulfilled as the result of a previousinformation crowd sourcing process. If the crowd sourcing moduledetermines that the current user request and the earlier request areidentical or sufficiently similar, the crowd sourcing module attempts tofulfill the current user request in a similar manner. The similaritybetween the two user requests can be determined based on the similaritybetween the domains and properties that were activated during thenatural language processing (intent inference) process for the two userrequests.

In some embodiments, the query generation module uses the results fromthe natural language processing (intent inference) process to determinewhat questions will likely bring forth answers that will aid the digitalassistant in producing a satisfactory response to the user request. Forexample, suppose that the user had said, “Find me a restaurant thatserves Caipirinha.” The digital assistant would be able to infer thatthe user wishes to find a restaurant, but would not know what“Caipirinha” stands for. The query generation module will then use thewords that were not found in the vocabulary for the property of“restaurant” (e.g., “Caipirinha” in this case) as the subject of thequery. Therefore, the query generation module would generate queriessuch as “What is Caipirinha?” or “What kind of food or drink is called“Caipirinha”? in addition to the user's original question “Whichrestaurant serves Caipirinha.” In some embodiments, for unknownvocabulary, the query generation module may also provide a fewvariations of the spelling in the different queries.

In addition, in some embodiments, the query generation components mayalso use the ontology (e.g., the hierarchy of nodes and sub-nodes) ofthe “restaurant” property to formulate a query such as “What kind ofcuisine is Caipirinha?” When a reply is received for this query and thedigital assistant finds out that “Caipirinha” is a type of alcoholicdrink found in South America, then, the digital assistant will be ableto focus its search for restaurants that serve South American cuisineand alcoholic beverages, and finally find an answer to the user'soriginal question.

In some embodiments, the query generation component 504 may pass atleast some of the domains and properties that were activated duringnatural language processing process of the user request to a CSinformation source that is capable of extracting information embedded inone or more partially completed structured queries representing thosedomains and properties. The CS information source can use theinformation extracted from the partially completed structured queries todecide which contributor should receive the natural language orstructured crowd sourcing queries generated by the query generationmodule 604. In some embodiments, the CS information source can also usethe extracted information to provide answers to follow-up questions thata contributor may ask regarding the crowd sourcing queries.

There are many ways that queries can be generated for a user request.Different query generation methods may have different implications inthe effectiveness in bringing forth information helpful in responding touser requests, and in the efficiency of query dispatching and answercollection processes. The methods for generating queries provided aboveare just some of the examples. In some embodiments, the query generationmodule 504 stores all the queries that have been handled, and thequeries that are in the answer collection stage in a queries database(e.g., the queries database 520 in FIG. 5). The queries in the queriesdatabase may be clustered based on their commonalities in terms ofsubject matter. In some embodiments, equivalent or similar queries maybe identified as such in the queries database.

In some embodiments, the crowd sourcing module selects the suitable CSinformation sources for a user request before the queries for the userrequest are generated. In some embodiments, however, the queries can begenerated from the user request first, and then CS information sourcescan be selected for each query based on various criteria, e.g., based onthe subject matter of the query. In some embodiments, a group of CSinformation sources can be selected before the queries are generated,and the selected CS information sources can be further refined andassigned to handle different queries after the queries are generated.

For example, the queries that are generated for a user request may berelated to several different domains of knowledge. Thus, differentqueries may be dispatched to different CS information sources eachsuitable for answering some of the queries. Suppose that a user asked,“How do I program my thermostat to conserve energy?” This user requestmay be partially matched to the domain of technical support due to theterms “program” and “thermostat,” and partially matched to the domain ofenergy conservation due to the terms “conserve energy” in the userrequest. Based on these two domains, the CS information source selectionmodule may decide to select a technical support forum for a first query“How to program a thermostat?” generated from the user request, whileselecting a conservation focused panel for a second query “What roomtemperatures conserve energy?” generated from the user request.

Referring back to FIG. 6A, after the queries are generated for eachselected CS information source, the queries and selected CS informationsources are optionally presented to the user for review and approval(612). In some embodiments, the user is allowed to modify the queriesand/or the selection of CS information sources at this time or acceptthe queries and CS information sources as suggested by the crowdsourcing module. In some embodiments, the digital assistant does notpresent the queries and selected CS information sources to the user forreview or approval, and simply proceeds to send the queries to the CSinformation sources.

In some embodiments, natural language representations of the queries arepresented to the user for review and approval, but equivalent structuredmachine-readable queries are sent to the CS information sources. In someembodiments, the crowd sourcing module will present a list of all thedifferent CS information sources available to the user upon request bythe user. The user is allowed to choose one or more of the CSinformation sources to use for the crowd sourcing. In some embodiments,the user is also allowed to modify some or all of the queries that areto be sent to the different CS information sources.

In some embodiments, if the information crowd sourcing module is alreadyhandling a similar user request, and the queries generated for thecurrent user request are already being answered at various CSinformation sources, the information crowd sourcing module notifies theuser that many users have been experiencing the same issue or have thesame question, and assures the user that a solution or answer should bearriving soon.

In some embodiments, if the crowd sourcing module is invoked as part ofthe failure management process of a digital assistant, the crowdsourcing module stores the user request in a failure status database.Each user request is assigned a unique identifier in the failure statusdatabase, and information associated with the user request, such as theoriginal user input, the context information associated with the userrequest, the reason of the failure, the partial or complete intentinferred from user input, the current status of the crowd sourcing forthe user request, and ultimately, the result of the crowd sourcing andoutput generation, can be stored in the failure status database. Variousmodules of the crowd sourcing module can updated the failure statusdatabase with appropriate information.

In some embodiments, when a user's voice input or a portion thereof isincluded a query, the query generation module implements an algorithm toclean the voice input of the user to remove or obfuscate any offensiveor sensitive phrases or words from the voice input before releasing thevoice input to any public forums. In some embodiments, the querygeneration module establishes a screening process that prevents queriesinvolving questionable content from being posted onto the public forums.For example, if the query generation module detects sensitive wordsrelated to illegal activities (e.g., copyright violations ordrug-related activities) in a user request, the information crowdsourcing component can reject the user request, or avoid sending queriesrelated to the user request to the CS information sources.

Referring back to FIG. 6A, if suitable CS information sources and/orqueries have been presented to the user for approval, the crowd sourcingmodule determines whether the user has approved the CS informationsources and/or queries presented to the user (614). If the presented CSinformation sources and/or queries are approved, the crowd sourcingmodule can prepare to enter the second stage of the information crowdsourcing described with reference to FIG. 6B. If the user did notapprove the CS information sources and queries, but provided suggestionsfor modifications, the digital assistant modifies the queries and/orselection of CS information sources according to user's suggestions(616). After the queries and/or selection of CS information sources aremodified according to the user's suggestions, the crowd sourcing modulecan prepare to enter the second stage of the information crowd sourcingdescribed with reference to FIG. 6B. If the request for approval of theCS information sources and/or queries is not implemented by the crowdsourcing module, the crowd sourcing module proceeds to the second stageonce the queries are generated.

In some embodiments, if a query generated for a current user request isa duplicate of another query already provided to an CS informationsource, the information crowd sourcing module does not sent that queryto the CS information source again, but simply monitors the answers forthe earlier query for both some earlier user request(s) and the currentuser request.

FIG. 6B illustrates the second stage of the information crowd sourcingprocess for the user request. The second stage can continue from thestep 616 in FIG. 6A or the “No” branch of the decision 614 in FIG. 6A.In the second stage, the information crowd sourcing module optionallysearches the crowd-sourced knowledge base (e.g., the crowd-sourcedknowledge base 358 in FIGS. 3A-3B and 5 and 7) to see if any of theapproved queries for the current user request already exists in thecrowd-sourced knowledge base (622). If one or more of the approvedqueries already exist or have equivalents in the crowd-sourced knowledgebase 358, the information crowd sourcing module uses the answers tothose queries as the answers for the one or more approved queries.

If one or more of the approved queries are not found in thecrowd-sourced knowledge base 358, the information crowd sourcing moduleproceeds to send those one or more approved queries to the approved CSinformation sources (624). In some embodiments, sending the queries to aCS information source includes posting a message to a public discussionforum or bulletin board, adding a challenge to a game arena, sending astructure query to a database, and/or other communications according tothe respective APIs and protocols of the CS information sources.

After the queries have been dispatched to the CS information sources,the information crowd sourcing module (or an answer monitoring modulethereof) monitors the replies received from the CS information sourcesfor the queries dispatched to the CS information sources (626). In someembodiments, the monitoring includes responding to follow-up questionsabout the queries, and determining when to close the answer gatheringperiod for each of the queries. In some embodiments, the monitoring alsoinvolves requesting the user to provide additional information orparticipating in a live dialogue with one of the CS information sources.In some embodiments, the monitoring also includes determining whether itis appropriate to request the user to provide information or participatein a live dialogue based on the current state of the user. For example,the answer monitoring module may access the user's calendars anddetermine the user's current location or speed or current engagement,and decide whether it is appropriate to interrupt the user at thepresent time.

In some embodiments, after the queries are sent to different CSinformation sources, the answer monitoring module proactively (e.g.,periodically) check to see if any replies or answers have been providedby the CS information sources. Sometimes, the answer monitoring modulereceives notifications from the CS information sources when one or morereplies or answers have been received for a particular query. Sometimes,if the number of answers are abundant for certain CS informationsources, e.g., expert forums having many self-identified experts orpopular game arenas, the answer monitoring module optionally sets alimited answer gathering period and stops taking more replies or answersafter the limited answer gathering period expires. For different typesof CS information sources, the manner by which answer to queries aremonitored may be different.

In some embodiments, the answer monitoring module 506 providesadditional information to the CS information sources in response tofollow-up questions received from the CS information sources. Forexample, when a reply is received from a CS information source about aquery, the answer monitoring module may utilize the natural languageprocessing capabilities and intent inference process of the digitalassistant to determine whether the reply seeks additional clarificationinformation or provides an answer. If the reply seeks additionalinformation, the answer monitoring module determines what information isbeing sought and whether the digital assistant possesses thatinformation. If the information is available, and sharing of theinformation with the CS information source is not prohibited by theuser's privacy policy or preference, the answer monitoring moduleprovides the information to the CS information source. In someembodiments, the answer monitoring module formulates a natural languageresponse that includes the requested additional information and providesthe natural language response back to the CS information source. Forexample, the natural language response can be posted as a follow-up tothe original query in a public forum, and all users reading the originalquery can now see the follow-up information as well.

In some embodiments, when the answer monitoring module processes thefollow-up questions to determine their meanings and intent, the answermonitoring module also utilizes the domains and properties associatedwith the original user request to help provide context to the follow-upquestions.

In some embodiments, when a follow-up question is received from a CSinformation source for a particular query, the answer monitoring moduleinitiates a dialogue with the information source (e.g., an expert or acustomer support staff). In some embodiments, the answer monitoringsystem utilizes the natural language processing and dialogue processingcapabilities of the digital assistant to facilitate the dialogue withthe CS information source. In some embodiments, the dialogue can becarried out continuously in real-time, or intermittently over anextended period of time. For example, the answer monitoring module mayengage in a diagnostic process with a live technical support staff, andcan answer questions posed by the technical support staff. In someembodiments, the digital assistant handles the follow-up questionswithout the active participation of the user in some situations, whilein other situations, the answer monitoring module may decide that it isappropriate to bring the user into the follow-up dialogue, so that theuser can provide information that the digital assistant did notcurrently possess.

Referring back to FIG. 6B, once the information crowd sourcing module(or the answer monitoring module thereof) determines that the enoughanswers have been gathered for the queries associated with a userrequest, the information crowd sourcing module proceeds to compile andintegrate the answers received for the queries, and formulate a responseto the user request based on the integrated answers (628). In someembodiments, the answer integration module 508 compiles and integratesthe answers received from the queries for the user request intoconsolidated crowd sourced information, and the response generationmodule generates the response to the user request based on theconsolidated crowd sourced information and any information the digitalassistant already possessed before the information crowd sourcing forthe user request.

In some embodiments, when integrating the answers received fromdifferent CS information sources, the answer integration module mergesand reconciles information received from all of the answers. In someembodiments, outlier answers may be filtered out. In some embodiments,the answer integration module ranks the answers according to factorssuch as the votes they received, the quality-level or credibility oftheir respective contributors, the number of duplicates or supporterreplies each answer has, and so on.

In some embodiments, one or more top-ranked answers are selected and aresponse is formulated to include all of the one or more top-rankedanswers (e.g., when the user request seeks an informational answer). Insome embodiments, one or more top-ranked answers are merged into asingle answer and a response is formulated to include the single answer.In some embodiments, the answers received for a user request may berelated to different aspects of the user request, and the responsegeneration module creates a response based on the consolidated knowledgeinferred from answers received for the different aspects of the userrequest.

In some embodiments, if the user request is for the performance of atask, the information obtained from the answers can be used by theresponse generation module to generate a task flow for the user request.

At the second stage shown in FIG. 6B, the response is merely formulated.For a user request seeking an informational answer, the crowd sourcedinformational answer has not been presented to the user. For a userrequest seeking performance of a task, the actions for performing thetask have not been carried out by the digital assistant.

Referring back to FIG. 6B, after the digital assistant has made theattempt to formulate a response to the user request using the crowdsourced information (e.g., information from the crowd-sourced knowledgebase and/or from the answers to the queries), the crowd sourcing moduledetermines whether the response has been successfully formulated (630).If the information crowd sourcing module determines that, despite theadditional information obtained from the answers received from the CSinformation sources, it is not able to successfully formulate a responseto the user request, the information crowd sourcing notifies the digitalassistant of the failure, and ceases further action on this user request(632). In some embodiments, the information crowd sourcing module storesa log of the failures, and retries the crowd sourcing at a later time(e.g., when a new CS information source is added).

In some embodiments, the answer integration module may determine that,at the end of the allowed answer gathering time period, no satisfactoryanswer or an insufficient number of answers have been received from thedifferent CS information sources. If so, the crowd sourcing module alsodetermines that a response cannot be successfully formulated at thistime. For example, sometimes, the answer integration module maydetermine that the answers received are not of sufficient quality (e.g.,based on a threshold quality criterion such as votes, or based on peerreview) for generating a satisfactory response to the user. Sometimes,the response generation module may determine that the answers receiveddo not provide sufficient information or the information is not ofsufficient specificity and clarity to help the response generationmodule to generate a task flow to accomplish the requested task.

Referring back to FIG. 6B, if the information crowd sourcing moduledetermines that it is able to formulate a response to the user requestwith the help of the additional information obtained from the crowdsourcing process, the digital assistant can prepare to enter the finalstage of providing the crowd sourced response to the user. FIG. 6Cillustrates the final stage of the information crowd sourcing, i.e.,presenting the crowd sourced response to the user.

As shown in FIG. 6C, before the digital assistant provides the crowdsourced response to the user, the digital assistant first determineswhether it is an appropriate time to reengage the user regarding theearlier user request (642). For example, the digital assistant maydetermine the current status of the user based on various signals suchas the speed by which the user is moving, the current location of theuser, the current time, whether there is any meeting scheduled on theuser's calendar for the present time, whether the user device iscurrently idle or being used for other tasks, and so on. Based on thestatus of the user, the digital assistant determines whether it isappropriate to interrupt the user or wait for a better opportunitylater.

If the digital assistant determines that is it not appropriate toactively reengage the user at the present time, the digital assistantoptionally presents a silent notification to the user (e.g., a silentstatus indicator on the user device) about the availability of the crowdsourced response, such that the user can initiate a dialogue with thedigital assistant at a time he or she deems fit or the digital assistantcan just wait until a suitable opportunity to reengage with the user ispresented (644). For example, at the end of a conversation with the userregarding other matters, the digital assistant can ask the user whetherthe user wishes to see the crowd sourced response for an earlier userrequest. In some embodiments, the digital assistant checks periodicallyto determine if it is appropriate the reengage the user regarding thecrowd sourced response for the earlier user request.

If the digital assistant determines that it is appropriate to reengagethe user regarding the earlier user request, the digital assistantproceeds to provide the crowd sourced response to the user. Depending onwhether the user request was seeking an informational answer or theperformance of a task, different steps are taken by the digitalassistant. For example, the digital assistant first determines whetherthe user request was for an informational answer (648). If the userrequest seeks an informational answer, the digital assistant proceeds topresent the informational answer formulated based on the crowd sourcedinformation to the user (650).

Alternatively, if the user request seeks the performance of a task, thedigital assistant optionally presents the task flow formulated for theuser request, and asks the user to confirm that the user wishes toproceed with the execution of the task flow (652). The digital assistantdetermines whether the user approves the task flow (654). If the userdoes not approve the task flow, the digital assistant considers thecrowd sourcing answer as unsatisfactory, and ceases further actionsregarding the user request (662). If the digital assistant determinesthat the task flow is approved, the digital assistant proceeds toexecute the task flow, e.g., using the facilities of the task flowprocessing and execution infrastructure of the digital assistant (656).

In some implementations, before a crowd sourced task flow is executedfor a user request, the crowed sourcing module forwards the crowdsourced task flow to a board of trusted experts for review andverification. In some embodiments, the digital assistant optionallysimulates an operating environment of the user's device and associatedservices, and executes the crowd sourced task flow in a simulationbefore the task flow is executed on the user's device.

Once either the informational answer is presented to the user, or theexecution of the proposed task flow is successfully completed, thedigital assistant asks the user whether the response provided for theuser request was satisfactory (658). If, based on the user's feedback,the digital assistant determines that the crowd sourced response isstill not satisfactory to the user, the digital assistant considers thecrowd sourced response an unsatisfactory response, and ceases furtheractions regarding the user request, or escalates the user request toother remedial processes, such as sending the user request to a humanpersonal assistant of the user, or the like.

Alternatively, if the digital assistant determines that the user issatisfied with the crowd sourced response (e.g., based on the user'sfeedback), the digital assistant (e.g., the knowledge-base buildingmodule 512 shown in FIG. 5) proceeds to record the crowd sourcedresponse, the user request, and/or the queries and answers thatcontributed to the successful fulfillment of the user request to thecrowd-sourced knowledge base (660). In some embodiments, the digitalassistant also records other information, such as the CS informationsources that provided the best answers to the queries, and the follow-upquestions and answers exchanged during the answer gathering stage.

In some embodiments, the user requests, queries, and answers can beorganized by one or more ontologies. The ontologies optionally model theuser requests, queries and answers as nodes of domains and propertieshaving various associated vocabulary, attributes, parameters, and taskflows. The nodes are organized in one or more hierarchies and may beinterrelated by their super-properties, sub-properties, vocabulary,attributes, parameters, task flows, and so on. The organization of thecrowd-sourced knowledge base allows the digital assistant to determinewhether satisfactory answers to a query or information request alreadyexist in the crowd-sourced knowledge base, and whether a satisfactoryresponse for a user request for performance of a task can be found inthe crowd-sourced knowledge base. In some embodiments, the searching fora user request or answer in the crowd-sourced knowledge ontology basedon words in the user request or answer is analogous to theidentification of an actionable intent based on the words in the user'sutterance. For example, a user request node in the crowd-sourcedknowledge ontology is optionally associated with one or more propertynodes that define different aspects of the user request. Based on therespective vocabulary associated with each node in the crowd-sourcedknowledge ontology, a new user request will trigger or activate many ofthe property nodes associated with an existing user request in thecrowd-sourced knowledge base, and be identified as similar to theexisting user request.

In some embodiments, the failure to provide a satisfactory response to auser request is identified by an offline analysis of the data logs ofthe operation and usage of the digital assistant, rather than in realtime while responding to the user. In some embodiments, the failure isidentified by automated or manual analysis of log data. For example, insome embodiments, failures are identified in the usage logs by evidencefor a task not being completed, or answers not being available frominformation sources, or from users' repeated requests with similarintents, or from users indicating frustration (e.g., turning off orotherwise interrupts the digital assistant's response or action), and/orother data analysis techniques. In some embodiments, data on failuresidentified offline are sent to the crowd-sourcing module in a similarmanner as failures identified in real-time. In these embodiments, theresults of crowd-sourced answers are available for use in futurereal-time sessions, but generally are not delivered back to the userswho experienced the failures that were identified offline.

FIG. 7 illustrates that, in some embodiments, the digital assistant 326or the components responsible for providing immediate response to theuser are provided separately (e.g., by third-parties, or on differentinfrastructures) from the failure management module 340, the crowdsourcing module 342, and the crowd-sourced knowledge base 358. However,the functionalities provided by the failure management module 340, thecrowd sourcing module 342, and the crowd sourced knowledge base 358herein, are also applicable to embodiments in which the failuremanagement module 340, the crowd sourcing module 342, and/or the crowdsourced knowledge base 358 are part of the digital assistant system 326.

Referring now to FIG. 7, content of the usage logs 370 of the digitalassistant 326 can be analyzed for failures 720. In some embodiments, afailure detection component 720 periodically, or upon request, scans theusage logs 370 for signals, and/or patterns indicative of failures. Forexample, if the digital assistant implements a catch-all response (e.g.,sending the user to a web search interface whenever the digitalassistant has failed to find or provide a satisfactory response aftertwo attempts), each issuance of the catch-all response is logged in theusage log 370. When the failure detection module 720 makes a queryagainst the usage logs 370 for all instances in which a catch-allresponse has been provided for a particular type of user request, thefailure detection module 720 would receive event logs of user requeststhat had failed to produce a satisfactory response. The failuredetection module 720 optionally sends to the failure management module340 the event logs of these user requests. In some embodiments, thefailure management module 340 processes these failures in a similarmanner as failures that are detected in real-time. The differencebetween real-time and offline processing of the failures lies in thelack of direct interactions with the user (e.g., requesting permissions,or confirmations, etc.) during the processing. In addition, in mostscenarios, if and when these failures are resolved by crowd-sourcing,the correct responses are not presented back to users who initiallyexperienced those failures. Instead, the offline processing is for thebenefit of users that make similar requests in the future.

In some embodiments, the information obtained by failure-drivencrowd-sourcing and stored in crowd-sourced knowledge base 358 isintegrated back into the real-time response mechanisms (e.g., the STTprocessing module 330, the natural language processing module 332,dialogue processing module 334, the task flow processing module 336, theservice processing module 356, and the models used by these processingmodules) used by the digital assistant 326. For example, if a questionposed by a user was not answered by the digital assistant, and thefailure was identified and sent to crowd sourcing as described herein,and successfully answered, then the answer is optionally added to theprimary information sources used by the digital assistant. The next timethat the same question is asked, the assistant optionally uses theupdated information sources to answer the question directly withoutinvoking the crowd-sourcing processes.

In some embodiments, a crowd-sourcing incorporation module 730 isimplemented, either as a standalone module or part of the failuremanagement module 340. The crowd-sourcing incorporation module 730operates in a batch mode to analyze successful crowd-sourced informationin the crowd-sourced knowledge base 358, and add that information intothe appropriate information stores (e.g., acoustic models, speechmodels, vocabulary, ontology, language models, task flow models, servicemodels, etc.) of the digital assistant 326.

In some embodiments, the crowd-sourced information is used to update thevocabulary database 344 used in natural language processing. Forexample, when the failure involved a failure to recognize certain wordswhich were then later associated by the crowd-sourcing process withnodes in the ontology, as described in earlier parts of thespecification, the new vocabulary is optionally added to vocabularydatabase 344 and indexed by nodes in ontology 360 so that on subsequentrequests the assistant can correctly infer the intent based on the newvocabulary.

In some embodiments the crowd-sourced information is used to update thetask flow models 354. For example, the crowd-sourced response to adiagnostic problem might suggest that the next task flow step is to askthe user a certain question to obtain specific diagnostic data. In someembodiments, such task flows are added to the task flow models 354 afteran expert panel review, as described in earlier part of thespecification.

In some embodiments the crowd-sourced information is used to update theservice models 356 used by service processing module 338. For example,the crowd-source response to a certain question might be a referral touse a particular service. For instance, if the user asked “where can Iget blue suede shoes” the crowd-sourced response might be a referral touse a service representing an online shoe sales information site. Thisis different from storing a specific answer to where to buy the shoes.It is the information used by an assistant to call the shoe salesinformation service with a query about blue suede shoes, which in turnmight respond with stores and inventory results for blue suede shoes.

In some embodiments, the crowd-sourced knowledge base 358 is useddirectly by external services which are employed by the digitalassistant to respond to user requests. For example, a digital assistantsometimes calls on the services of several question answering services(e.g., “external services” in FIG. 3B). One of these services isoptionally a question answering service that uses questions and answersstored in the crowd-sourced knowledge base 358 to provide answers backto the digital assistant 358. In these embodiments, some of thesequestions are directly answered by the assistant in real-time withoutrequiring the information to be incorporated into the responsemechanisms of the digital assistant 326 (e.g., through the operation ofthe crowd-sourcing incorporation module 730). For example, in someembodiments, the digital assistant optionally uses the information inthe crowd-sourced knowledge base 358, either directly or through athird-party service, for a period of time to respond to real-timerequests, and further evaluate the user feedback before allowing thecrowd-sourcing incorporation module 730 to modify the response mechanismof the digital assistant 326 using such information.

In some embodiments, when preparing a response to the user, the digitalassistant, in addition to calling on a web search engine on unknownqueries, also calls a service that is powered by a crowd-sourcedknowledge base (e.g., the crowd-sourced knowledge base 358 or otherthird-party crowd-sourced knowledge bases). In some embodiments, if thecrowd-sourced knowledge base powered service returns an answer, thedigital assistant provides that answer to the user instead of the websearch (e.g., the catch-all response). If the crowd-sourced knowledgebase powered service does not return an answer, the digital assistantprovides the answer received from the web search (e.g., the catch-allresponse). In this way, the competence of the digital assistant can beautomatically grown as it feeds failures to crowd-sourced services andthey provide suitable responses on subsequent requests.

It should be understood that the particular order in which theoperations in the flow charts have been described is merely exemplaryand is not intended to indicate that the described order is the onlyorder in which the operations could be performed. One of ordinary skillin the art would recognize various ways to reorder the operationsdescribed herein. In addition, unless explicitly specified, featuresdescribed in various embodiments may be combined and used together.Additionally, it should be noted that details of other processesdescribed herein may be applied in addition to, instead of, or inconjunction with the operations described with reference to the figures.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for providing a response to a userrequest, comprising: at a server computer with one or more processorsand memory: receiving a user request from a mobile client device, theuser request including at least a speech input and seeks aninformational answer or performance of a task; detecting a failure toprovide a satisfactory response to the user request; in response todetecting the failure, crowd-sourcing information relevant to the userrequest by querying one or more crowd sourcing information sources;receiving one or more answers from the crowd sourcing informationsources; and generating a response to the user request based on at leastone of the one or more answers received from the one or more crowdsourcing information sources.
 2. The method of claim 1, whereincrowd-sourcing the information relevant to the user request furthercomprises: generating one or more queries based on the user request; andsending the one or more queries to the one or more crowd sourcinginformation sources.
 3. The method of claim 1, wherein thecrowd-sourcing further comprises identifying, from a set of crowdsourcing information sources, the one or more crowd sourcing informationsources to be queried.
 4. The method of claim 1, further comprising,prior to the crowd-sourcing: requesting permission from the user to sendthe information contained in the user request to the one or more crowdsourcing information sources; and receiving permission from the user tosend the information contained in the user request to the one or morecrowd sourcing information sources.
 5. The method of claim 1, furthercomprising: receiving at least one real-time answer from a real-timeanswer-lookup database; upon receipt of the at least one real-timeanswer, sending to the mobile client device the at least one real-timeanswer; receiving at least one non-real-time answer from a non-real-timeexpert service after receiving the at least one real-time answer; andupon receipt of the at least one non-real-time answer, sending to themobile client device the at least one non-real-time answer.
 6. Themethod of claim 1, further comprising: not receiving any answer from atleast one of the one or more crowd sourcing information sources beforegenerating the remedial response.
 7. The method of claim 1, furthercomprising: when more than one answer is received from the one or morecrowd sourcing information sources, ranking the answers in accordancewith predetermined criteria.
 8. The method of claim 1, wherein receivingthe one or more answers from the crowd sourcing information sourcesfurther comprises: receiving at least one of the one or more answersfrom individual members of the public in non-real-time.
 9. Anon-transitory computer-readable medium storing instructions, theinstructions, when executed by one or more processors, cause theprocessors to perform operations comprising: receiving a user requestfrom a mobile client device, the user request including at least aspeech input and seeks an informational answer or performance of a task;detecting a failure to provide a satisfactory response to the userrequest; in response to detecting the failure, crowd-sourcinginformation relevant to the user request by querying one or more crowdsourcing information sources; receiving one or more answers from thecrowd sourcing information sources; and generating a response to theuser request based on at least one of the one or more answers receivedfrom the one or more crowd sourcing information sources.
 10. Thecomputer-readable medium of claim 9, wherein crowd-sourcing theinformation relevant to the user request further comprises: generatingone or more queries based on the user request; and sending the one ormore queries to the one or more crowd sourcing information sources. 11.The computer-readable medium of claim 9, wherein the crowd-sourcingfurther comprises identifying, from a set of crowd sourcing informationsources, the one or more crowd sourcing information sources to bequeried.
 12. The computer-readable medium of claim 9, wherein theoperations further comprise: prior to the crowd-sourcing: requestingpermission from the user to send the information contained in the userrequest to the one or more crowd sourcing information sources; andreceiving permission from the user to send the information contained inthe user request to the one or more crowd sourcing information sources.13. The computer-readable medium of claim 9, wherein the operationsfurther comprise: receiving at least one real-time answer from areal-time answer-lookup database; upon receipt of the at least onereal-time answer, sending to the mobile client device the at least onereal-time answer; receiving at least one non-real-time answer from anon-real-time expert service after receiving the at least one real-timeanswer; and upon receipt of the at least one non-real-time answer,sending to the mobile client device the at least one non-real-timeanswer.
 14. The computer-readable medium of claim 9, wherein the methodfurther comprise: not receiving any answer from at least one of the oneor more crowd sourcing information sources before generating theremedial response.
 15. The computer-readable medium of claim 9, whereinthe operations further comprise: when more than one answer is receivedfrom the one or more crowd sourcing information sources, ranking theanswers in accordance with predetermined criteria.
 16. Thecomputer-readable medium of claim 9, wherein receiving the one or moreanswers from the crowd sourcing information sources further comprises:receiving at least one of the one or more answers from individualmembers of the public in non-real-time.
 17. A system, comprising: one ormore processors; and memory storing instructions, the instructions, whenexecuted by the one or more processors, cause the processors to performoperations comprising: receiving a user request from a mobile clientdevice, the user request including at least a speech input and seeks aninformational answer or performance of a task; detecting a failure toprovide a satisfactory response to the user request; in response todetecting the failure, crowd-sourcing information relevant to the userrequest by querying one or more crowd sourcing information sources;receiving one or more answers from the crowd sourcing informationsources; and generating a response to the user request based on at leastone of the one or more answers received from the one or more crowdsourcing information sources.
 18. The system of claim 17, whereincrowd-sourcing the information relevant to the user request furthercomprises: generating one or more queries based on the user request; andsending the one or more queries to the one or more crowd sourcinginformation sources.
 19. The system of claim 17, wherein thecrowd-sourcing further comprises identifying, from a set of crowdsourcing information sources, the one or more crowd sourcing informationsources to be queried.
 20. The system of claim 17, wherein theoperations further comprise: prior to the crowd-sourcing: requestingpermission from the user to send the information contained in the userrequest to the one or more crowd sourcing information sources; andreceiving permission from the user to send the information contained inthe user request to the one or more crowd sourcing information sources.21. The system of claim 17, wherein the operations further comprise:receiving at least one real-time answer from a real-time answer-lookupdatabase; upon receipt of the at least one real-time answer, sending tothe mobile client device the at least one real-time answer; receiving atleast one non-real-time answer from a non-real-time expert service afterreceiving the at least one real-time answer; and upon receipt of the atleast one non-real-time answer, sending to the mobile client device theat least one non-real-time answer.
 22. The system of claim 17, whereinthe operations further comprise: not receiving any answer from at leastone of the one or more crowd sourcing information sources beforegenerating the remedial response.
 23. The system of claim 17, whereinthe operations further comprise: when more than one answer is receivedfrom the one or more crowd sourcing information sources, ranking theanswers in accordance with predetermined criteria.
 24. The system ofclaim 17, wherein receiving the one or more answers from the crowdsourcing information sources further comprises: receiving at least oneof the one or more answers from individual members of the public innon-real-time.